State the multiple regression model for this data and interpret all the slope coefficients.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
One economic development strategy suggests that some economies benefit from having government policies that promote growth in manufacturing and growth in exports. A
Country
Growth GDP (Y)
Growth Manufacturing (X1)
Growth Exports (X2)
China
11.88
7.081
13.968
Hong Kong
7.242
10.404
6.448
Japan
10.71
5.7
13.02
Korea
8.73
12.445
15.18
Malaysia
4.557
8.16
9.31
Singapore
6.466
5.723
7.776
Thailand
7.49
7.533
12.032
Mean
8.15
8.15
11.10
A summary of the multiple regression analysis undertaken in Excel is reported below.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.71724576
R Square
0.51444148
Adjusted R Square
0.27166223
Standard Error
2.14232238
Observations
7
ANOVA
df
SS
MS
F
Significance F
Regression
2
19.4501989
9.72509947
2.11896802
0.23576707
Residual
4
18.3581808
4.58954519
Total
6
37.8083797
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
4.1438
3.9408
1.0515
0.3524
-6.7976
15.0852
X1
-0.2435
0.3558
-0.6843
0.5314
-1.2314
0.7444
X2
0.5398
0.2676
2.0171
0.1139
-0.2032
1.2828
State the multiple regression model for this data and interpret all the slope coefficients.
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